“CV25 brings computer vision at the edge into the mainstream,” said Fermi Wang, President and CEO of Ambarella. “With this new SoC, we are sharply focused on reducing our customer’s overall system cost for delivery of significant computer vision performance, high-quality image processing and advanced cyber-security features at very low power. CV25-based cameras are capable of performing Artificial Intelligence (AI) at the edge, allowing features like facial recognition to happen in real-time on the device, rather than in the cloud.”

AI applications enabled at the edge include person recognition and the ability to distinguish between pets, persons, and vehicles. In smart video doorbells, CV25 can automatically recognize familiar faces approaching the front door, flag unknown persons, and alert the homeowner when a package is delivered. In driver monitoring systems, it can detect a driver’s drowsiness or level of distraction by monitoring their eyes and facial expressions.

CV25 delivers efficient video encoding in both AVC and HEVC formats with very low bitrates to minimize cloud storage costs. A high-performance Image Signal Processor (ISP) delivers outstanding imaging in low light conditions, and High Dynamic Range (HDR) processing extracts maximum image detail in high-contrast scenes. It includes a full suite of advanced cyber-security features to protect against hacking including secure boot, TrustZone™, and I/O virtualization. Based on 10nm ultra-low power process technology, the CV25 chip is optimized for wire-free camera applications that require long battery life and small form factors.

As part of the CVflow family, the CV25 chip shares a common SDK, Computer Vision (CV) tools, ISP, and cyber-security features with the existing CV22 and CV2 SoCs, allowing multiple price-performance options. Ambarella’s complete set of CV tools helps customers easily port their own neural networks onto CV25. The set includes a compiler, debugger, and support for industry-standard machine learning frameworks such as Caffe™ and TensorFlow™, with extensive guidelines for Convolutional Neural Networks (CNN) performance optimizations.

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